Flow cytometry issues

Flow cytometry issues

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I'm having problems with data analysis here.

I have flow cytometry data being collected on a Fortessa, and when I import them into FlowJo 8.7, all of my fluorescence values are systematically 10X lower than they are on the cytometer. No idea what's going on here, anybody can help? If you want screenshots and photos of the data, I'm happy to post them up.

I have this issue too with data from a Partec PAS III. I don't consider it a problem, as it's really just the x-axis which is one order of magnitude higher. At the end, all values coming from the machine are arbitrary values, so as long as the change is systematic, there's no problem.

The answer I have come to is the difference between FCS2.0 and FCS3.0 files. The binning pattern is different, and as such FCS2.0 files arbitrarily lowered my flow cytometry values compared to what I saw on the FACS DiVa software.

In the end, exporting with FCS3.0 was the way to go. If your software supports this, I'd highly recommend doing so!

Flow cytometry issues - Biology

Description of the Facility
FACS Facility
The Flow-Cytometry Facility is located on the Nineth floor, East wing of the Hunter North building. This Facility can provide analyses of up to 7 parameters in eukaryotic cell populations and sort cells under sterile conditions. These analyses can be used to identify and isolate rare cell populations, determine chromosome ploidy in individual cells, study apoptosis, and study cell-signaling, among other applications.

User training for the FACSCalibur(Becton-Dickinson Biosciences) and assistance in its operation is available, by appointment, through the Facility's Manager. Periodic user training on the FACSCalibur is also provided through specialized seminars.

The FACSVantage(Becton-Dickinson Biosciences) is operated only by the Facility Manager. The Manager and Project Director are available to discuss projects and to advise investigators on the applications relevant to their project.


FACSCalibur Flow Cytometer

FACSCalibur is a bench top analyzer consisting of Argon, air-cooled laser emitting at 488 nm and 633 nm diode laser. It can provide analyses up to 6 parameters and up to 4-colors.

FACSVantage Flow Cytometer

FACSVantage is a sorter consisting of Helium-Neon (HeNe) air-cooled laser emitting at 633 nm and an Enterprise II, water-cooled laser emitting at both 488 nm and Ultra Violet(UV). It can provide analyses up to 7 parameters and up to 5-colors.

Rules of operations
Rules and Regulations

1. Make an appointment to use the equipment at least one week in advance.

2. Equipment use is limited to a 2-hour period per laboratory per day unless special arrangements are authorized. Users must notify the Facility of any cancellation at least 6 hours in advance. For the FACSVantage, a set-up fee will be charged for a missed appointment.

3. Sign the log Book that is placed adjacent to all instruments.

4. After each session on the FACSCalibur, follow the instrument cleaning procedure.

5. The Principal Investigator of each lab will be responsible for his/her laboratory personnel.

6. Report any problems to the manager as soon as possible.

Safety Precautions

1. Lasers

a. FACSCaliubr is equipped with Air-cooled Argon laser emitting at 488 nm and a Diode laser emitting at 633 nm. Although the lasers are enclosed, the light may be visible (with difficulty) if the cover is opened. Do not look at the laser light. It can damage your eyes.

b. FACSVantage is equipped with Enterprise and HeNe water-cooled lasers, which are visible when the instrument is in operation. No one is allowed in the room without the presence of the Facility Manager while the instrument is in operation.

2. Specimens: Depending on the investigator's specimens, one needs to wear an appropriate protective clothing (latex gloves, lab coats, face shield etc.). No materials requiring BL2 should be brought to the Facility. Latex gloves should be used when changing fluids in the FACSCalibur.

Service Schedules and Required forms
FACS Facility Service Charges

FACSCalibur usage: $10.00 per hour

(minimum charge of $10 for each use)

FACSVantage Usage: $20.00 per hour + set-up fee of $20.00 (per day) (1/2 hr. minimum)

Flow cytometry issues with cell aggregates/clumping when using T cells

I routinely use flow cytometry with various cell types, but have mostly used adherent cells until recently. With adherent cells, I have to treat with EDTA or Trypsin-EDTA to detach the cells before staining/fixing anyway, so they usually end up as a fairly clean single cell suspension, especially if I pipette vigorously or pass the cells through a nylon mesh filter. Having recently begun using cells that grow in suspension, such as T cells (CEM-ss, SupT1, PBMCs), I've noticed that they are more prone to form aggregates, which interferes with my assay.

It is extremely important to know I definitely have a single cell suspension, no doublets, because I am looking for the formation of syncytia, which on a flow cytometer show up with an FSC/SSC profile very similar to doublets, i.e. they are large (high FSC-A) and have unusual shapes (high FSC-W) and granularity (high SSC) that most people would typically gate out of their analysis - I actually need to keep such unusual FSC/SSC events in my analysis, but need to exclude doublets or triplets, so it cannot simply be done by FSC/SSC gating. I am trying to optimize my negative control (where there are no syncytia) so that I am getting a clean signal on which I can gate to detect syncytia by looking for cells with high FSC/SSC, or by having two cell populations labeled with different color cytoplasmic dyes and looking for double-positive cells. Obviously cell aggregates interfere with both of these measures and it is really important for me to have a clean negative control.

Just to quickly explain the procedure, I transfer the T cells (which are growing in RPMI 1640 with 10% FBS) into 5 ml round-bottom FACS tubes. I centrifuge them for 5 minutes at 500 g, decant the supernatant, and resuspend in 200 ul serum-free RPMI containing DNase. After 10 minutes @ RT (and some gentle agitation), I add 200 ul PBS/8% PFA (4% final) to fix. 10 minutes later, I add 1.5 ml PBS/1% BSA, centrifuge for 5 minutes at 1500 g, decant the supernatant, and resuspend in PBS before analyzing by flow cytometry. (In some cases there are some antibody labeling steps after this, but I have determined that the aggregates are present immediately after fixation or even before, so I don't think those steps are relevant).

Here are the things I've tried, and how they turned out:

Trypsin-EDTA: If after the first centrifugation, before the DNase step, I treat with Trypsin-EDTA (the typical solution you would use to detach adherent cells), a really bizarre thing happens: the cells immediately (in seconds) clump up into one huge aggregate, which is then impossible to disperse by pipetting vigorously. If I then inactivate with RPMI/FBS, centrifuge, and continue with the DNase treatment, the clump is still there. I have also tried resuspending the cells in serum-free culture media before adding the trypsin (so that I am not adding it onto a cell pellet and so that it is diluted 1:1), but they still clump. I honestly don't understand this at all and nobody around here has any idea of why it's happening since you typically expect Trypsin to dissociate, not clump cells. I have even found papers that mention using Trypsin to eliminate doublets in the exact same assay I am doing (i.e. looking for syncytia in T cells), and they don't mention anything about clumping, or any other steps before or after trypsin that I'm not already doing.

Just EDTA without trypsin: it has been a while since I tried this, but I know that it does not cause that extreme clumping that I get when I use trypsin-EDTA. Still, the doublets are there in my negative control.

Nylon mesh filter: After the DNase step, and before adding the fixative, I have also tried passing the cells through a 50 um nylon mesh. While this definitely reduced the number of doublets/aggregates showing up on the flow cytometer (in my control with no syncytia), they are still there. Furthermore, because the syncytia I expect to see can easily be 50 um or more in diameter, I don't want to have these be excluded by the filter or be lysed by it. Basically using a set-size filtration step biases my experiment and I would rather avoid it (besides the fact that it doesn't seem to completely get rid of the aggregates).

At this point I am running out of ideas. I just ordered a sample of Accutase to try, but my guess is it will work pretty similarly to trypsin as it is really just a protease (unless anyone has experience with using it to break up T cell aggregates?). My suspicion is that the aggregates are forming not due to protein-based cell-cell linkages (which is what trypsin and EDTA are good against), and not due to extracellular DNA (since I eliminate that with DNase) but due to cell surface sugars (glycans). I am not aware of any routine methods for neutralizing or removing glycans, though.

TLDR: T cells are very sticky which is bad for my flow-based assay for detecting things that look like aggregates but are not. Trypsin makes it worse. HELP.

MIFlowCyt-EV: a framework for standardized reporting of extracellular vesicle flow cytometry experiments

Extracellular vesicles (EVs) are small, heterogeneous and difficult to measure. Flow cytometry (FC) is a key technology for the measurement of individual particles, but its application to the analysis of EVs and other submicron particles has presented many challenges and has produced a number of controversial results, in part due to limitations of instrument detection, lack of robust methods and ambiguities in how data should be interpreted. These complications are exacerbated by the field's lack of a robust reporting framework, and many EV-FC manuscripts include incomplete descriptions of methods and results, contain artefacts stemming from an insufficient instrument sensitivity and inappropriate experimental design and lack appropriate calibration and standardization. To address these issues, a working group (WG) of EV-FC researchers from ISEV, ISAC and ISTH, worked together as an EV-FC WG and developed a consensus framework for the minimum information that should be provided regarding EV-FC. This framework incorporates the existing Minimum Information for Studies of EVs (MISEV) guidelines and Minimum Information about a FC experiment (MIFlowCyt) standard in an EV-FC-specific reporting framework (MIFlowCyt-EV) that supports reporting of critical information related to sample staining, EV detection and measurement and experimental design in manuscripts that report EV-FC data. MIFlowCyt-EV provides a structure for sharing EV-FC results, but it does not prescribe specific protocols, as there will continue to be rapid evolution of instruments and methods for the foreseeable future. MIFlowCyt-EV accommodates this evolution, while providing information needed to evaluate and compare different approaches. Because MIFlowCyt-EV will ensure consistency in the manner of reporting of EV-FC studies, over time we expect that adoption of MIFlowCyt-EV as a standard for reporting EV- FC studies will improve the ability to quantitatively compare results from different laboratories and to support the development of new instruments and assays for improved measurement of EVs.

Keywords: Extracellular vesicles flow cytometry framework reporting standardization.

© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of The International Society for Extracellular Vesicles.


Overview of the MIFlowCyt EV…

Overview of the MIFlowCyt EV Reporting Framework. The left column shows each category…

Overview of the MIFlowCyt EV…

Overview of the MIFlowCyt EV Reporting Framework. The left column shows each category…

Summary of a poll about what extracellular vesicles (EV) flow cytometry (FCM) working…

Summary of a poll about what extracellular vesicles (EV) flow cytometry (FCM) working…

(a) Example plot of reporting…

(a) Example plot of reporting serial dilutions data, with event count per second…

(a) Example plot of reporting…

(a) Example plot of reporting serial dilutions data, with event count per second…

Best Practices for Multiparametric Flow Cytometry

Flow cytometry is an elegant quantitative technology, allowing the interrogation of single cells among tens of thousands or even millions of cells in minutes. Advantages of multiparameter flow cytometry include the ability to probe single cells with multiple functional markers, to correlate protein expression levels using multiple antibodies, and ultimately to more accurately define cell populations. Increasing the number of targets and fluorophores, however, also increases the complexity of the experiment and requires greater attention to detector optimization, panel design, controls, and other setup details. Here we describe a few best practices for designing a multiparameter flow cytometry experiment. While not comprehensive, they encompass some of the most important features of good experimental setup and panel design [1,2].

Voltage walk for highest-quality data

Cytometer manufacturers provide a performance test that certifies the instrument is performing optimally with respect to a precise set of specifications. Detector optimization takes this process a step further, enabling the highest-quality data to be obtained in each flow cytometer channel. For each detector, the voltage (or gain) chosen must provide the best separation between positive and negative signals and ensure all measurements are within the detector’s linear range. Typically, the voltage walk method (Figure 1) is used to determine the minimum voltage requirement (MVR) that allows clear resolution of dim fluorescent signals from the background noise of the instrument. In this method, dimly fluorescent beads are run using a series of increasing voltage settings, and the spread of the signal (or the coefficient of variation, CV) is plotted against the voltages. Decreasing the voltage for a detector below its MVR can result in the loss of resolution of dim populations, and increasing the voltage above its MVR gives no advantage for population resolution. Because this method does not ensure that the brightest signals do not exceed the upper limit of the detector’s range, alternative methods have been developed in which both unstained and brightly stained beads or cells are used to determine MVR [3,4].

Figure 1. Determination of the optimal voltage setting for a flow cytometer detector using a voltage walk. The voltage walk method shown uses a single dimly fluorescent hard-dyed bead. Data are acquired at each voltage setting increment in a given detector, and the percent robust coefficient of variation (%rCV) and robust standard deviation (rSD) are exported and plotted vs. voltage to visualize the point of inflection. The lowest voltage on the %rCV curve before the increase in the rSD should be used for the detector. In this example, the MVR is determined to be 300 mV (arrow).

Antibody titration in panel design

Antibody titration is also an important optimization technique for multiparameter flow cytometry and is the best way to minimize nonspecific binding and increase signal detection. It can also be used to minimize spillover spreading, which occurs when the signal from dyes that emit fluorescence over a broad range of wavelengths is captured in multiple detectors, complicating data interpretation. To perform a simple antibody titration, start with the manufacturer’s recommended concentration, perform serial 2-fold dilutions, and plot the stain index (SI), which is a measure of the relative brightness of a fluorophore-conjugated antibody [5]. The SI for a specific antibody– dye conjugate and its spillover spreading will help to determine if a separating concentration (at which negative and positive cells display the greatest difference in fluorescence), or a saturating concentration (at which the antibody has saturated the antigen available in the cells) of antibody should be used (Figure 2). A separating concentration provides good separation of labeled vs. unlabeled cells (e.g., when identifying percent-positive populations in immunophenotyping experiments), reduces spreading error, and conserves antibody. Saturating antibody concentrations—sometimes required for the detection of low-abundance antigens—can lead to increased spillover spreading and difficulty detecting dim signals in other detectors.

Figure 2. Antibody titration example.(A) Cells were incubated with serial dilutions of an APC-conjugated mouse anti-CD8 antibody, run on the Invitrogen Attune NxT Flow Cytometer, and analyzed using FlowJo software. Data are shown on a single graph for easier comparison. Note that the sample highlighted in box 1 is the saturation concentration of antibody, whereas the box 2 sample is the separation concentration. (B) The stain index (SI) was calculated for the data in (A) using the equation: (Mean (positive cells) – Mean (negative cells)) / (2 x SD (negative cells)), and plotted as a function of the antibody dilution.

Fluorophore selection and allocation

One of the biggest challenges in multiparameter flow cytometry is selecting the combinations of fluorophores and antibody conjugates that minimize the need for compensation and spillover adjustments without compromising data quality. The more dyes included in a flow cytometry panel, the more likely that spillover spreading will reduce the ability to distinguish the specific signal of one fluorophore in the presence of others. When choosing fluorescent labels:

  • Use bright fluorophores with antibodies for low-abundance targets and dim fluorophores with antibodies for highly expressed antigens
  • Minimize the spectral overlap of fluorophores to reduce spillover
  • Use fluorophores that are spectrally distinct for the detection of coexpressed markers
  • Use fluorophores that are spectrally similar for different cell subpopulations that will be gated separately

Figure 3 shows a method for visualizing spillover spreading error due to spectral overlap. Although commonly used, the tandem fluorophore PE-Cy®7 exhibits significant spreading due to low-energy (long-wavelength) photons, which in turn negatively impacts the resolution of fluorescent labels in other channels, especially those associated with poorly expressed antigens. Use of a spillover spread matrix is another way to visualize the spread into all other detectors for a given fluorophore [6].

Figure 3. Spreading error visualization. Single-stained samples were run on the Invitrogen Attune NxT Flow Cytometer and analyzed using FlowJo software. Data from each detector were combined into a single plot. (A) Staining with the FITC antibody conjugate appears robust when analyzed in the FITC detector minimal spreading error is observed in other channels. (B) Staining with PerCP-Cy®5.5 contributes high spreading error into the PE and BV711 channels. (C) Staining with BD Horizon Brilliant Violet 711 (BV711) contributes noticeable spread into the PerCP-Cy®5.5, APC, and PE channels. (D) Staining with PE-Cy®7 demonstrates extensive spreading error in multiple channels. *BD Horizon Brilliant Violet dyes (Becton, Dickinson and Company).

Controls, controls, controls

Controls—e.g., fluorescence minus one (FMO) controls, compensation controls, and viability controls—are critical for evaluating multiparameter flow cytometry data. FMO controls are required for setting gates when multiple fluorophores are used together and when markers are expressed on a continuum. They help to account for the signal introduced from all other fluorescent labels in the channel being gated. FMO controls, which contain all markers except the one of interest, can provide clarity for low-density or smeared populations and can help to delineate two populations that are not easily resolved. Also required in every multiparameter flow cytometry panel is a viability control, a fluorescent probe that specifically identifies dead cells so that they can be properly excluded from data analysis [7]. Dead cells are sticky and can nonspecifically bind antibodies and other probes, complicating the analysis (Figure 4).

Figure 4. Effects of viability gating on population statistics. The inclusion or exclusion of a viability dye can drastically affect population statistics obtained from an experiment, and discriminating live and dead cells using only scatter parameters can be subjective and inaccurate [7]. In this example from Perfetto et al. [7], after application of a lymphocyte gate (forward scatter vs. side scatter), live and dead cells were discriminated using the Invitrogen LIVE/DEAD Fixable Violet Dead Cell Stain Kit note the significant number of dead cells despite a scatter gate. Subsequent analysis of live cells and dead cells shows the dramatic difference in apparent phenotypes between the two cell populations. Reprinted from Perfetto SP, Chattopadhyay PK, Lamoreaux L Nguyen R, Ambrozak D, Koup RA, Roederer M (2006) J Immunol Methods 313:199–208, with permission from Elsevier.

Guidelines for flow cytometry

Follow these best practices for multiparameter flow cytometry:

  • Optimize the voltage settings for each flow cytometer detector
  • Titrate each antibody for optimal performance in the panel
  • Carefully consider the pairing of dyes with targets and minimize spillover spreading
  • Use FMO and viability controls to set gates correctly

Panel design is an iterative process that requires testing all combinations and reviewing the spillover spread matrix at each iteration. Resources—such as webinars, eLearning courses, instrument information, and a library of application notes and protocols—are available at the Flow Cytometry Learning Center. Additionally we have developed the Invitrogen Flow Cytometry Panel Builder (Figure 5), which guides you through flow cytometry panel design, providing a simplified, customizable experience to fit your needs.

Figure 5. Flow Cytometry Panel Builder—A tool for all flow cytometrists. Whether you are a novice or an expert, designing a panel for flow cytometry is a highly complex process. If you are a beginner, let the Invitrogen Flow Cytometry Panel Builder lessen your anxiety over panel building by making the pairing of markers and fluorophores quick and simple using a highly visual format. Are you an expert? Then you will appreciate using the Flow Cytometry Panel Builder to easily review the spectral signals and filters per laser line and check fluorophore spillover values per channel. With access to information on over 13,000 antibodies for flow cytometry, this tool allows quick selection of antibodies for flow cytometry panels.

Key sample considerations for flow cytometry staining

Figure 1. Using lineage markers to help define a rare population.
A) Staining of hematopoietic progenitors using BioLegend mouse lineage cocktail (CD3, GR1, CD11b, B220, Ter119) and anti-mouse CD34 antibody in mouse bone marrow B) Gating of human DC cells using BioLegend human lineage cocktail (CD3, CD14, CD19, CD20, CD56) and anti-human HLA-DR antibody.

Try using bivariate graphs (dot plot or pseudocolor plot) instead of histogram when gating populations expressing markers found on other cell types. For example, Figure 2 shows the difference between gating strategy of mouse dendritic cells using one dendritic marker CD11c on histogram or two dendritic cells markers, CD11c and MHC Class II, on a pseudocolor plot. Based on the bivariate plot, the percentage of true DCs (CD11 int MHCII + ) is 1.2% and not 4.5% DCs like shown on the histogram. The difference in percentage between the pseudocolor plot and histogram is a result of inaccurate gating of outliers (CD11c int MHCII +/- ) in the histogram. To know more about different options of plotting the data, read our blog Flow Cytometry Data Analysis I: What Different Plots Can Tell You.

Figure 2. Gating of dendritic cells using histogram and bivariate plots

  • Myeloid cells (monocytes, macrophages, dendritic cells and granulocytes) express Fcγ receptors which can bind to the Fc region of antibodies resulting in non-specific staining. To minimize this nonspecific staining, include FcR blocker in your staining when working with samples enriched in myeloid cells (i.e. bone marrow, blood, spleen or in vitro differentiation cultures of myeloid cells).
  • Some tandem dyes including PE/Dazzle™ 594, APC/Fire™ 750, PE/Cy7, PE/Cy5, PerCP/Cy5.5 and especially APC/Cy7, can non-specifically bind to monocytes and macrophages independently of Fc receptors. Use of True-Stain Monocyte Blocker™ in flow cytometry staining can help to minimize the nonspecific binding. Learn more with our scientific poster.

Figure 3. Autofluorescence of leukocyte populations observed in FITC channel. On the left, gating strategy of unstained mouse peripheral blood leukocyte subsets based on forward and side scatter. On the right, histogram overlay of autofluorescence between lymphocytes (red), monocytes (green), neutrophils (blue), and eosinophils (purple).


Measurement of CFSE + Events as a Strategy to Assess Sample Purity

The lack of consensus regarding methods for purification of EVs remains a challenge, especially when reproducibility between different isolation modalities is desired. This limits EV use in clinical applications and is further complicated by the insufficient means to measure sample purity, notably in complex biological samples containing a mixture of vesicular and non-vesicular particles, such as plasma. Multiple strategies, such as measurement of protein:particle ratio (Webber and Clayton, 2013 Maiolo et al., 2015) and albumin (Baranyai et al., 2015 Veremeyko et al., 2018), have been proposed as quality control parameters for the preparation of EVs, particularly when low specificity isolation methods are employed (Théry et al., 2019). However, because they fail to provide population information, it is still unclear whether these metrics can accurately reflect the purity of preparations of EVs.

CFSE has to date been frequently used as a strategy to label EVs for FC applications (Pospichalova et al., 2015 Morales-Kastresana et al., 2017 Mastoridis et al., 2018 Morales-Kastresana et al., 2019). The suitability of different dyes to efficiently stain and identify EVs has already been addressed by the Jennifer Jones group (Morales-Kastresana et al., 2017). In a work published in 2017, the Jones’ group tested several dyes, including CFSE and PHK26. While PKH26 was shown to produce 100� nm micelles or aggregates both in the presence of EVs or alone in solution, CFSE alone did not form such aggregates. Thus, taking this into account, in our work EVs were labeled with CFSE. The data here presented supports the vesicular labeling with CFSE as a means for determining quality control of purification protocols of EVs. As shown, the majority of particles present in samples purified by high-specificity methods were CFSE + . The employment of ExoQuick ® or ultracentrifugation for enrichment of EVs prior to SEC reduced the proportion of CFSE – particles. In addition, lysis of EVs with the detergent NP-40 resulted in a reduction of 90% of CFSE + events. Together, these results support the usefulness of CFSE as a tool to identify vesicular particles in biofluids, and thus the suitability of our FC strategy for quality control and quantitative comparison of isolates of EVs prepared by different protocols.

This same approach was employed to validate the depletion of EVs from the FBS used in our in vitro studies. Previous reports suggest that traces of bovine EVs may persist in the FBS supernatant after depletion steps (Shelke et al., 2014), which could interfere in the analysis of EVs from conditioned medium. However, comparison of serum-free medium and medium containing 10% of EVs-depleted FBS showed an equally reduced proportion of CFSE + events. This indicates that residual FBS-derived EVs was not high enough to impact our analysis. Nonetheless, future studies using our strategy for analysis of EVs in conditioned medium will need to account for background events on a case-by-case basis, especially when using low cell numbers, short conditioning time and/or cells producing low levels of EVs.

Results presented by others indicate that CFSE staining could not label 100% of EVs (van der Vlist et al., 2012 Pospichalova et al., 2015 de Rond et al., 2018). Thus, we reasoned that some of the CFSE – events observed in our experiments could correspond to non-stained EVs. However, unlike the results presented here, in these previous studies CFSE staining was performed in cells before (van der Vlist et al., 2012) or during medium conditioning (Pospichalova et al., 2015), and CFSE concentrations were 1.6� times lower (van der Vlist et al., 2012 Pospichalova et al., 2015 de Rond et al., 2018) than those we used. In addition, only �% of the events in samples purified by differential ultracentrifugation coupled with sucrose cushion (UC-I) were CFSE-, and treatment of EVs with the detergent NP-40 caused a reduction of 90% of CFSE + events, indicating that this dye labels the majority of EVs in our experimental settings. While this may still suggest that CFSE is not capable of staining all EVs present in the sample, it is still unclear to which extent even high-purity isolation methods may provide 100% pure preparations of EVs. Although undesirable, protein aggregation may be present in antibody preparations. This is mainly due to solution conditions, such as ionic strength, pH, temperature, pressure and excipients (Manning et al., 1995), and intrinsic properties of antibodies, such as primary sequence, tertiary structure, non-symmetrical hydrophobicity and charge distributions (Liu et al., 2005 Roberts, 2007 Nishi et al., 2010). Therefore, the potential contribution of unbound antibody aggregates to CFSE – Antibody + events was tested. Most CFSE – CD9 + events didn’t correspond to unbound antibodies, and were reduced by EVs purification (Supplementary Figure S5). These results suggest that the CFSE – events observed in the CD9 + population analysis correspond to non-vesicular particles of specific molecular composition with similar size as compared to EVs. Future studies, including detailed morphology and composition analysis, will be necessary to further define these CFSE – non-vesicular particles.

Comparison of Purification Methods of EVs by FC

In spite of being considered a high-recovery and low-specificity method (Théry et al., 2019), isolation based on precipitation polymers such as ExoQuick ® resulted in a high proportion of CFSE + EVs. This agrees with studies suggesting that EVs prepared by ultracentrifugation or precipitation polymers are comparable (Helwa et al., 2017 Prendergast et al., 2018). In samples prepared by ExoQuick ® , however, we found that the proportion of CD9 + CFSE + events was reduced when compared to NP and other isolation methods. This suggests that, despite providing a high yield of EVs, ExoQuick ® may insert EV population bias. Also of concern, precipitating agents have previously been linked to potential loss of biological activity (Paolini et al., 2016) and structure (Gamez-Valero et al., 2016) of EVs. Thus, our data adds yet another parameter that should be carefully considered before selecting ExoQuick ® as a method of choice for the isolation of EVs.

SEC is the technique of choice for many groups interested in studying the composition and biological activity of vesicles, as it allows simple, fast and affordable isolation of EVs. As SEC is a key component of our FC strategy, the proportion of CFSE + particles in our preparations was measured to access the isolation efficacy of EVs by SEC. In our experimental settings, this preparation is referred to as NP (non-purified), since SEC is used after staining, and not before as a traditional isolation method. Although considered a low recovery, high specificity method (Théry et al., 2019), conditioned medium processed by SEC contained less than 40% of CFSE + vesicular particles. This was also the case for more complex samples, such as plasma and vitreous humor, in which the percentage of CFSE + particles after SEC processing were, respectively, �% and �%. These findings agree with recent studies using comparative transmission electron microscopy, in which SEC-derived preparations displayed a lower proportion of structures resembling EVs when compared to samples derived from differential ultracentrifugation (Takov et al., 2019).

Differential ultracentrifugation is one of the most commonly used EV purification methods. To improve EV purity, most researchers combine ultracentrifugation with additional techniques following the primary step, such as the use of washing steps with saline as well as the use of density gradients (Thery et al., 2018). We found that the proportion of CFSE + events and CD9 + events within the CFSE + gate did not differ in the absence (UC-III) or presence (UC-II and UC-IV) of washing steps with PBS or when a sucrose cushion step was used (UC-I). Our results suggest that these additional steps have no major impact in sample purity. However, a more detailed characterization of the potential impact of these washing and/or separation steps in the selection of populations of EVs with specific composition (protein, sugar, lipid, and nucleic acids) will be necessary in future studies.

Our FC strategy allows for faster processing times and also substantially decreases the sample volume requirements compared to conventional EVs isolation protocols (Table 1). Thus, we consider ours to be a consistent approach to be applied to control the quality of preparations of EVs.

While we used CFSE as a general EV marker, our experimental setup was performed having CD9 not only as an illustration of the capabilities of the method, but also as an analyte of interest. Our side-by-side comparison of CFSE + CD9 + events in samples submitted (UC-I) or not (NP) to isolation by ultracentrifugation showed that both strategies are comparable when analyzing CD9 + EVs populations (Figures 4C,D). However, although this equivalence was true for CD9 + EVs, we cannot discard the possibility that other populations of EVs behave differently, specially while probably there is no validated universal EV marker that can be used as an experimental control. Every method, including differential ultracentrifugation, potentially inserts population isolation bias to EVs (as illustrated in Figure 3C). Such isolation bias, if existent, has yet to be studied and understood. Be it for future studies in the field of population of EVs, or be it for studies involving other vesicular molecules, we believe and strongly suggest that side-by-side validation of non-purified versus purified samples (as in Figures 4C,D) should be performed as a pre-validation of our approach. Thus, for every EV molecule of interest, the approach presented in our work that relies on the use of NP samples in different contexts should first be validated by a comparison study similar to the one presented in Figures 4C,D.

Longitudinal Study of EVs in Plasma by FC

Longitudinal composition analyses can provide precious temporal information on the dynamics of EVs in physiological and pathological settings (Eitan et al., 2017 Menon et al., 2018 Wu et al., 2018). However, these studies are often difficult in microvolumes of samples, mainly due to the limited number of EVs that can be harvested in these experimental conditions (Théry et al., 2019). This constraint frequently leads to insufficient recovery of EVs (Clayton et al., 2018), unless small volume samples are pooled from multiple individuals or collections. Moreover, in studies involving small animals, the requirement for lethal bleeding in order to collect enough plasma for the effective isolation of EVs complicates the performance of longitudinal studies and increases the demand for animals, leading to higher costs, higher sample processing complexity and potential bioethical issues. By not requiring isolation of EVs prior to staining, our FC strategy allows for the analysis of both intra- and inter-individual heterogeneity in the population of interest throughout an experiment. In our studies, the proportion of CFSE + EVs and of CD9 + events within CFSE + EVs increased in the plasma of mice bearing liver metastatic pancreatic cancer lesions. Based on these results, we are currently studying the potential use of these readouts for follow-up studies of pancreatic cancer patients in the metastatic phase.

Study of EVs in Vitreous Humor by FC

The vitreous humor is a small-volume biofluid that contains low protein content, ranging from 120 to 500 ng/μL (Chowdhury et al., 2010), which is frequently considered to arise from filtration of plasma through fenestrated capillaries of the ciliary body stroma via the iris root (Freddo et al., 1990). Besides the quantitative differences in protein content, a comparison of vitreous humor and plasma proteome revealed that only 58% of the vitreous humor proteins have also been identified in human plasma (Chowdhury et al., 2010). Consistent with this, our analysis revealed that vitreous fluid contains three times more CFSE + vesicular structures when compared to plasma. Furthermore, it contained insignificant levels of CD9 + events, comparable to those found in control solutions with only CFSE and anti-CD9. These results are consistent with the previously reported absence of CD9 in EVs from vitreous humor (Murthy et al., 2014 Skeie et al., 2015 Zhao et al., 2018). However, it is still unknown whether the absence of CD9 + vesicles is a result of the filtration that occurs during the production of vitreous humor, uptake and degradation of this vesicle population by ocular cells, higher prevalence of non-endosomal EVs and/or other mechanisms. Although it is unclear to which extend ocular cells contribute to the collection of EVs found in vitreous humor, our FC strategy can be potentially used to study these vesicles both in pre-clinical and clinical settings as potential biomarkers and biological mediators of eye diseases.

Concluding Remarks

In conclusion, as you continue to optimize your flow cytometry experiments, take some time to focus on the blocking of the cells to minimize FcR mediated binding. This can be done with inexpensive human IgG serum quite easily. You can add the blocking reagent, incubate the cells for some period of time before adding the labeling reagents, and don’t have to do an extra wash, just some recalculating of the volumes.

More importantly, if you encounter some unusual staining patterns, the cause may be worth exploring. It could be the cells binding the fluorochrome, or it could be an antibody binding a fluorochrome- as shown in the PD-L1 example. Fortunately, there is a simple workaround to this, and that’s to add a second blocking reagent to minimize this effect.

It is important in the optimization phase to be extremely critical of any issues or differences you see, as you don’t want to carry something through to your production panel that could have been addressed in the optimization. Blocking is a necessary step, but often overlooked and based on lab tradition. Armed with this new knowledge, go forth and block better.

To learn more about important control measures for your flow cytometry lab, and to get access to all of our advanced materials including 20 training videos, presentations, workbooks, and private group membership, get on the Flow Cytometry Mastery Class wait list.

Tim Bushnell holds a PhD in Biology from the Rensselaer Polytechnic Institute. He is a co-founder of—and didactic mind behind—ExCyte, the world’s leading flow cytometry training company, which organization boasts a veritable library of in-the-lab resources on sequencing, microscopy, and related topics in the life sciences.


Chronic lymphocytic leukemia (CLL) is characterized by the accumulation of malignant B cells. The disease generally affects elderly people and shows an indolent clinical course. 1 Despite encouraging therapeutic advances, CLL still is considered an incurable disease. 2 A number of prognostic markers such as IGHV mutational status, expression of ZAP-70, CD38, and β2-microglobulin as well as certain cytogenetic abnormalities (e.g., del17p) have been described. 3 More recent studies have presented a new prognostic score (CLL-IPI) and novel genetic markers (e.g., NOTCH1 and TP53 mutations). 4, 5

While immunochemotherapy has been the standard first-line treatment of CLL for many years, novel therapies targeting B cell receptor (BCR) signaling such as ibrutinib and idelalisib play an increasingly important role. 6 Along these lines, several recent studies have focused on altered signal transduction through the BCR and consecutively impaired calcium flux in CLL. Le Roy et al. demonstrated that in vitro stimulation of the BCR results in calcium flux and nuclear factor of activated T cells (NFAT) activation and could show that DNA binding capacity of NFAT2 correlates with clinical outcome. 7 Another study has recently demonstrated that surface IgM expression and intracellular calcium mobilization are dependent on IGHV mutational status and DNA methylation. 8 Multiple laboratories have furthermore characterized anergy, a state of BCR unresponsiveness and impaired calcium mobilization, as a hallmark of indolent CLL with favorable outcome. 9, 10 Previous studies have primarily analyzed calcium mobilization in PBMCs without gating for CLL cells or required a time-consuming isolation of CLL cells before the measurements. Furthermore, ratiometrics with FuraRed by calculation of the ratio between bound and unbound calcium has been established for PBMCs 11 and provides several advantages, although has not been used in the context of CLL. 12

In the current study, we have established a flow cytometry-based assay to evaluate intracellular calcium flux capabilities in primary CLL cells employing a cohort of 25 patients. The degree of calcium flux was subsequently correlated with well-established prognostic parameters and clinical outcome. Using a receiver operating characteristics (ROC) analysis, we defined a cutoff value to discriminate a responder from a nonresponder population with significant differences in progression-free survival (PFS). In summary, we provide a novel assay to quickly and reliably assess BCR signaling in CLL cells as a means to predict clinical outcome.

Flow Cytometry: Going with the Flow

Sara Bowen, PhD, is a biochemist with what can only be described as a giddy excitement for her job. She runs the flow cytometry facility at St. Joseph’s Hospital and Barrow Neurological Institute in Arizona, and she positively lights up when talking about “flow” and her lasers.

Dr. Sara Bowen. Photo by Cathy Seiler.

An analogy to understand flow cytometry is to think about a stream filled with lots of different fish: big fish and small fish, black fish and white fish. To better understand the characteristics of the fish in the stream, you could direct each fish into a small channel in the stream where they can pass through in single file. From looking at them one at a time, you can note if the fish is big or small or black or white. Flow cytometry is exactly like this. Instead of fish, Sara is looking at hundreds of thousands of cells. Instead of a stream and a small channel, she is using a flow cytometry machine. And instead of her eyes and a notebook, she’s using lasers and flow cytometry software. And yes, her job really is that cool.

We had a chance to talk with Sara about being a scientist, how she got interested in flow cytometry, why flow is so important, and why she’s so obsessed with lasers.

Cathy Seiler: You have your PhD in biochemistry, but not all biochemists do the same thing. What does being a biochemist mean to you?

Dr. Sara Bowen: When I think of biochemists, I sort us into two main buckets. Some are trying to understand parts of a picture. They are looking at a very specific piece of biology and trying to figure out how it does what it does. For example: How does the shape of a hemoglobin protein affect its ability to hold on to and release oxygen? Biochemists in the other bucket are asking questions about the picture as a whole. They want to know how the parts fit together to make biological processes work. For example, what hormones are released when you eat a tangelo, and how do they help you digest it? But zoomed in or zoomed out, all biochemists are trying to understand how biology works, be it human, penguin, jellyfish, or tulip tree biology.

Seiler: How did you first become interested in flow cytometry?

Flow cytometry image of blood cells detecting two different markers (CD3 and CD19) that light up different colors using different color lasers. This experiment separated different types of blood cells called T cells and B cells. Credit: Sara Bowen.

Bowen: Ever had to give a book report? Well, that doesn’t stop when you get to graduate school. I had to read a scientific paper about flow cytometry and explain it to my classmates, and at the time, I had never even heard of flow. When I was getting started, all I could think about was that the data looked like someone had sneezed on a page. How does anybody make sense of all those dots?! But in the process of learning about flow well enough to explain it to my classmates, I realized that it is a powerful tool for scientists. And when it hit me that the whole thing is possible because of all the colors in the rainbow, I was in love.

Seiler: What is with your obsession with lasers?

Bowen: Did I mention that I like colors? Each of my lasers is a specific color: violet, blue, or red. The cells (or molecules that are attached to the cells) that I’m looking at interact with different colors of light differently depending on the cells’ or molecules’ physical properties. That sounds complicated, so think of it this way. Your boss comes in and says, “What is going on in here?” Depending on the tone of voice, you will probably react differently. If your boss sounds angry, you might react defensively. If your boss is laughing, you might laugh back. If your boss is clearly talking to himself as he or she wanders by, you might not react at all. A big part of my job is figuring out how to combine laser color and cells or molecules so that I can gauge the “reactions” of the cells or molecules in a way that I can understand. My job is a game, and lasers make it possible to play.

[Cathy’s note: If you think of the fish analogy above, the lasers are what detect the color of the fish in the stream.]

Also, just say out loud: “I work with lasers.” Sounds impressive, right?

Seiler: How can flow cytometry and your work help people?

Bowen: I think a good way to start answering this question is by listing some of the things flow cytometry has already done for people:

  • It was crucial for understanding and diagnosing HIV/AIDS.
  • It was instrumental during the Human Genome Program.
  • It has been critical in understanding, diagnosing, and treating leukemia and lymphoma.

All three of those contributions are massively important to humankind. In my work, I’m helping other scientists use flow to combat serious health issues including stroke, Multiple Sclerosis, brain cancer, and transplant rejection.

Seiler: What is one thing you really want readers to know about you, flow cytometry, or science in general?

Bowen: Scientists are just regular people. Most of us aren’t particularly brilliant, but we are persistent. We’re all trying to understand things that have never been explained before. We do that by building on what’s already known and testing all the possibilities that might add knowledge. That means most days science is more tedious than exciting.

Seiler: What advice would you give to someone who is interested in pursuing a career like yours?

Bowen: Be persistent. Sometimes (or a lot of times) you will fail, and that’s OK. It might feel like you are the only one struggling, but I promise you’re not.

—Dr. Cathy Seiler is the manager of the Biobank Core Facility at Barrow Neurological Institute and St. Joseph’s Hospital. She received her bachelor’s degree in biochemistry and molecular biology at Boston University and her PhD at the Watson School of Biological Sciences at Cold Spring Harbor Laboratory, studying cancer. In her spare time, Cathy is the editor for ISBER News and writes about science and the life of a scientist on her blog Things I Tell My Mom .

Watch the video: Κυκλοφορικό Σύστημα: Λευκά Αιμοσφαίρια-Αιμοπετάλια-Πλάσμα (September 2022).


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